Lab4
Justin Stott
February 26, 2020
Examples
library(ggplot2)
library(tidyverse)## -- Attaching packages ---------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## v purrr 0.3.3## -- Conflicts ------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(DT)
###############################################################################Title
bp <- ggplot(PlantGrowth, aes(x=group, y=weight)) + geom_boxplot()
bp
bp + ggtitle("Plant growth")
## Equivalent to
# bp + labs(title="Plant growth")
# If the title is long, it can be split into multiple lines with \n
bp + ggtitle("Plant growth with\ndifferent treatments")
# Reduce line spacing and use bold text
bp + ggtitle("Plant growth with\ndifferent treatments") +
theme(plot.title = element_text(lineheight=.8, face="bold"))
###############################################################################Axes
bp <- ggplot(PlantGrowth, aes(x=group, y=weight)) +
geom_boxplot()
bp
bp + coord_flip()
# Manually set the order of a discrete-valued axis
bp + scale_x_discrete(limits=c("trt1","trt2","ctrl"))
# Reverse the order of a discrete-valued axis
# Get the levels of the factor
flevels <- levels(PlantGrowth$group)
flevels## [1] "ctrl" "trt1" "trt2"#> [1] "ctrl" "trt1" "trt2"
# Reverse the order
flevels <- rev(flevels)
flevels## [1] "trt2" "trt1" "ctrl"#> [1] "trt2" "trt1" "ctrl"
bp + scale_x_discrete(limits=flevels)
# Or it can be done in one line:
bp + scale_x_discrete(limits = rev(levels(PlantGrowth$group)))
bp + scale_x_discrete(breaks=c("ctrl", "trt1", "trt2"),
labels=c("Control", "Treat 1", "Treat 2"))
# Hide x tick marks, labels, and grid lines
bp + scale_x_discrete(breaks=NULL)
# Hide all tick marks and labels (on X axis), but keep the gridlines
bp + theme(axis.ticks = element_blank(), axis.text.x = element_blank())
# Make sure to include 0 in the y axis
bp + expand_limits(y=0)
# Make sure to include 0 and 8 in the y axis
bp + expand_limits(y=c(0,8))
# Set the range of a continuous-valued axis
# These are equivalent
bp + ylim(0, 8)
# bp + scale_y_continuous(limits=c(0, 8))
# These two do the same thing; all data points outside the graphing range are
# dropped, resulting in a misleading box plot
bp + ylim(5, 7.5)## Warning: Removed 13 rows containing non-finite values (stat_boxplot).
#> Warning: Removed 13 rows containing non-finite values (stat_boxplot).
# bp + scale_y_continuous(limits=c(5, 7.5))
# Using coord_cartesian "zooms" into the area
bp + coord_cartesian(ylim=c(5, 7.5))
# Specify tick marks directly
bp + coord_cartesian(ylim=c(5, 7.5)) +
scale_y_continuous(breaks=seq(0, 10, 0.25)) # Ticks from 0-10, every .25
# Reverse order of a continuous-valued axis
bp + scale_y_reverse()
# Setting the tick marks on an axis
# This will show tick marks on every 0.25 from 1 to 10
# The scale will show only the ones that are within range (3.50-6.25 in this case)
bp + scale_y_continuous(breaks=seq(1,10,1/4))
# The breaks can be spaced unevenly
bp + scale_y_continuous(breaks=c(4, 4.25, 4.5, 5, 6,8))
# Suppress ticks and gridlines
bp + scale_y_continuous(breaks=NULL)
# Hide tick marks and labels (on Y axis), but keep the gridlines
bp + theme(axis.ticks = element_blank(), axis.text.y = element_blank())
# Create some noisy exponentially-distributed data
set.seed(201)
n <- 100
dat <- data.frame(
xval = (1:n+rnorm(n,sd=5))/20,
yval = 2*2^((1:n+rnorm(n,sd=5))/20)
)
# A scatterplot with regular (linear) axis scaling
sp <- ggplot(dat, aes(xval, yval)) + geom_point()
sp
# log2 scaling of the y axis (with visually-equal spacing)
library(scales) # Need the scales package##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorsp + scale_y_continuous(trans=log2_trans())
# log2 coordinate transformation (with visually-diminishing spacing)
sp + coord_trans(y="log2")
sp + scale_y_continuous(trans = log2_trans(),
breaks = trans_breaks("log2", function(x) 2^x),
labels = trans_format("log2", math_format(2^.x)))
set.seed(205)
n <- 100
dat10 <- data.frame(
xval = (1:n+rnorm(n,sd=5))/20,
yval = 10*10^((1:n+rnorm(n,sd=5))/20)
)
sp10 <- ggplot(dat10, aes(xval, yval)) + geom_point()
# log10
sp10 + scale_y_log10()
# log10 with exponents on tick labels
sp10 + scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x)))
# Data where x ranges from 0-10, y ranges from 0-30
set.seed(202)
dat <- data.frame(
xval = runif(40,0,10),
yval = runif(40,0,30)
)
sp <- ggplot(dat, aes(xval, yval)) + geom_point()
# Force equal scaling
sp + coord_fixed()
# Equal scaling, with each 1 on the x axis the same length as y on x axis
sp + coord_fixed(ratio=1/3)
bp + theme(axis.title.x = element_blank()) + # Remove x-axis label
ylab("Weight (Kg)") # Set y-axis label
# Also possible to set the axis label with the scale
# Note that vertical space is still reserved for x's label
bp + scale_x_discrete(name="") +
scale_y_continuous(name="Weight (Kg)")
# Change font options:
# X-axis label: bold, red, and 20 points
# X-axis tick marks: rotate 90 degrees CCW, move to the left a bit (using vjust,
# since the labels are rotated), and 16 points
bp + theme(axis.title.x = element_text(face="bold", colour="#990000", size=20),
axis.text.x = element_text(angle=90, vjust=0.5, size=16))
# Label formatters
library(scales) # Need the scales package
bp + scale_y_continuous(labels=percent) +
scale_x_discrete(labels=abbreviate) # In this particular case, it has no effect
# Self-defined formatting function for times.
timeHMS_formatter <- function(x) {
h <- floor(x/60)
m <- floor(x %% 60)
s <- round(60*(x %% 1)) # Round to nearest second
lab <- sprintf('%02d:%02d:%02d', h, m, s) # Format the strings as HH:MM:SS
lab <- gsub('^00:', '', lab) # Remove leading 00: if present
lab <- gsub('^0', '', lab) # Remove leading 0 if present
}
bp + scale_y_continuous(label=timeHMS_formatter)
# Hide all the gridlines
bp + theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank())
# Hide just the minor gridlines
bp + theme(panel.grid.minor=element_blank())
# Hide all the vertical gridlines
bp + theme(panel.grid.minor.x=element_blank(),
panel.grid.major.x=element_blank())
# Hide all the horizontal gridlines
bp + theme(panel.grid.minor.y=element_blank(),
panel.grid.major.y=element_blank())
###############################################################################Legend
# Remove legend for a particular aesthetic (fill)
bp + guides(fill=FALSE)
# It can also be done when specifying the scale
bp + scale_fill_discrete(guide=FALSE)
# This removes all legends
bp + theme(legend.position="none")
bp + scale_fill_discrete(breaks=c("trt1","ctrl","trt2"))
# These two methods are equivalent:
bp + guides(fill = guide_legend(reverse=TRUE))
bp + scale_fill_discrete(guide = guide_legend(reverse=TRUE))
# You can also modify the scale directly:
bp + scale_fill_discrete(breaks = rev(levels(PlantGrowth$group)))
# Remove title for fill legend
bp + guides(fill=guide_legend(title=NULL))
# Remove title for all legends
bp + theme(legend.title=element_blank())
bp + scale_fill_discrete(name="Experimental\nCondition")
bp + scale_fill_discrete(name="Experimental\nCondition",
breaks=c("ctrl", "trt1", "trt2"),
labels=c("Control", "Treatment 1", "Treatment 2"))
# Using a manual scale instead of hue
bp + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"),
name="Experimental\nCondition",
breaks=c("ctrl", "trt1", "trt2"),
labels=c("Control", "Treatment 1", "Treatment 2"))
# A different data set
df1 <- data.frame(
sex = factor(c("Female","Female","Male","Male")),
time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),
total_bill = c(13.53, 16.81, 16.24, 17.42)
)
# A basic graph
lp <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex)) + geom_line() + geom_point()
lp
# Change the legend
lp + scale_shape_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man"))
# Specify colour and shape
lp1 <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex, colour=sex)) + geom_line() + geom_point()
lp1
# Here's what happens if you just specify colour
lp1 + scale_colour_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man"))
# Specify both colour and shape
lp1 + scale_colour_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man")) +
scale_shape_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man"))
pg <- PlantGrowth # Copy data into new data frame
# Rename the column and the values in the factor
levels(pg$group)[levels(pg$group)=="ctrl"] <- "Control"
levels(pg$group)[levels(pg$group)=="trt1"] <- "Treatment 1"
levels(pg$group)[levels(pg$group)=="trt2"] <- "Treatment 2"
names(pg)[names(pg)=="group"] <- "Experimental Condition"
# View a few rows from the end product
head(pg)## weight Experimental Condition
## 1 4.17 Control
## 2 5.58 Control
## 3 5.18 Control
## 4 6.11 Control
## 5 4.50 Control
## 6 4.61 Control#> weight Experimental Condition
#> 1 4.17 Control
#> 2 5.58 Control
#> 3 5.18 Control
#> 4 6.11 Control
#> 5 4.50 Control
#> 6 4.61 Control
# Make the plot
ggplot(data=pg, aes(x=`Experimental Condition`, y=weight, fill=`Experimental Condition`)) +
geom_boxplot()
# Title appearance
bp + theme(legend.title = element_text(colour="blue", size=16, face="bold"))
# Label appearance
bp + theme(legend.text = element_text(colour="blue", size = 16, face = "bold"))
bp + theme(legend.background = element_rect())
bp + theme(legend.background = element_rect(fill="gray90", size=.5, linetype="dotted"))
bp + theme(legend.position="top")
# Position legend in graph, where x,y is 0,0 (bottom left) to 1,1 (top right)
bp + theme(legend.position=c(.5, .5))
# Set the "anchoring point" of the legend (bottom-left is 0,0; top-right is 1,1)
# Put bottom-left corner of legend box in bottom-left corner of graph
bp + theme(legend.justification=c(0,0), legend.position=c(0,0))
# Put bottom-right corner of legend box in bottom-right corner of graph
bp + theme(legend.justification=c(1,0), legend.position=c(1,0))
# No outline
ggplot(data=PlantGrowth, aes(x=group, fill=group)) +
geom_bar()
# Add outline, but slashes appear in legend
ggplot(data=PlantGrowth, aes(x=group, fill=group)) +
geom_bar(colour="black")
# A hack to hide the slashes: first graph the bars with no outline and add the legend,
# then graph the bars again with outline, but with a blank legend.
ggplot(data=PlantGrowth, aes(x=group, fill=group)) +
geom_bar() +
geom_bar(colour="black", show.legend=FALSE)
###############################################################################Colors
# Remove legend for a particular aesthetic (fill)
bp + guides(fill=FALSE)
# It can also be done when specifying the scale
bp + scale_fill_discrete(guide=FALSE)
# This removes all legends
bp + theme(legend.position="none")
bp + scale_fill_discrete(breaks=c("trt1","ctrl","trt2"))
# These two methods are equivalent:
bp + guides(fill = guide_legend(reverse=TRUE))
bp + scale_fill_discrete(guide = guide_legend(reverse=TRUE))
# You can also modify the scale directly:
bp + scale_fill_discrete(breaks = rev(levels(PlantGrowth$group)))
# Remove title for fill legend
bp + guides(fill=guide_legend(title=NULL))
# Remove title for all legends
bp + theme(legend.title=element_blank())
bp + scale_fill_discrete(name="Experimental\nCondition")
bp + scale_fill_discrete(name="Experimental\nCondition",
breaks=c("ctrl", "trt1", "trt2"),
labels=c("Control", "Treatment 1", "Treatment 2"))
# Using a manual scale instead of hue
bp + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"),
name="Experimental\nCondition",
breaks=c("ctrl", "trt1", "trt2"),
labels=c("Control", "Treatment 1", "Treatment 2"))
# A different data set
df1 <- data.frame(
sex = factor(c("Female","Female","Male","Male")),
time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),
total_bill = c(13.53, 16.81, 16.24, 17.42)
)
# A basic graph
lp <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex)) + geom_line() + geom_point()
lp
# Change the legend
lp + scale_shape_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man"))
# Specify colour and shape
lp1 <- ggplot(data=df1, aes(x=time, y=total_bill, group=sex, shape=sex, colour=sex)) + geom_line() + geom_point()
lp1
# Here's what happens if you just specify colour
lp1 + scale_colour_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man"))
# Specify both colour and shape
lp1 + scale_colour_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man")) +
scale_shape_discrete(name ="Payer",
breaks=c("Female", "Male"),
labels=c("Woman", "Man"))
pg <- PlantGrowth # Copy data into new data frame
# Rename the column and the values in the factor
levels(pg$group)[levels(pg$group)=="ctrl"] <- "Control"
levels(pg$group)[levels(pg$group)=="trt1"] <- "Treatment 1"
levels(pg$group)[levels(pg$group)=="trt2"] <- "Treatment 2"
names(pg)[names(pg)=="group"] <- "Experimental Condition"
# View a few rows from the end product
head(pg)## weight Experimental Condition
## 1 4.17 Control
## 2 5.58 Control
## 3 5.18 Control
## 4 6.11 Control
## 5 4.50 Control
## 6 4.61 Control#> weight Experimental Condition
#> 1 4.17 Control
#> 2 5.58 Control
#> 3 5.18 Control
#> 4 6.11 Control
#> 5 4.50 Control
#> 6 4.61 Control
# Make the plot
ggplot(data=pg, aes(x=`Experimental Condition`, y=weight, fill=`Experimental Condition`)) +
geom_boxplot()
# Title appearance
bp + theme(legend.title = element_text(colour="blue", size=16, face="bold"))
# Label appearance
bp + theme(legend.text = element_text(colour="blue", size = 16, face = "bold"))
bp + theme(legend.background = element_rect())
bp + theme(legend.background = element_rect(fill="gray90", size=.5, linetype="dotted"))
bp + theme(legend.position="top")
# Position legend in graph, where x,y is 0,0 (bottom left) to 1,1 (top right)
bp + theme(legend.position=c(.5, .5))
# Set the "anchoring point" of the legend (bottom-left is 0,0; top-right is 1,1)
# Put bottom-left corner of legend box in bottom-left corner of graph
bp + theme(legend.justification=c(0,0), legend.position=c(0,0))
# Put bottom-right corner of legend box in bottom-right corner of graph
bp + theme(legend.justification=c(1,0), legend.position=c(1,0))
# No outline
ggplot(data=PlantGrowth, aes(x=group, fill=group)) +
geom_bar()
# Add outline, but slashes appear in legend
ggplot(data=PlantGrowth, aes(x=group, fill=group)) +
geom_bar(colour="black")
# A hack to hide the slashes: first graph the bars with no outline and add the legend,
# then graph the bars again with outline, but with a blank legend.
ggplot(data=PlantGrowth, aes(x=group, fill=group)) +
geom_bar() +
geom_bar(colour="black", show.legend=FALSE) Exercise 1
SNPs<- read.table("23andMe_complete.txt", header = TRUE, sep = "\t")
SNPs %>%
ggplot(.,aes(chromosome)) +
geom_bar(fill = "blue")+
labs(title = "Count of SNPs on Different Chromosomes", x = "Chromosome", y = "Count of SNPs")+
theme_bw()
Exercise 2
SNPs %>%
ggplot(.,aes(chromosome, fill = genotype))+
geom_bar(position = "stack")+
# scale_fill_manual(values = c("AA"="red","AG"="red","AT"="red","AC"="red","GG"="red","GA"="red","GT"="red","GC"="red","TA"="red","TG"="red","TT"="red","TC"="red","CG"="red","CA"="red","CT"="red","CC"="red","A"="blue","T"="blue","C"="blue","G"="blue", "D"="purple", "DD"="purple", "DI"="purple", "I"="purple", "II"="purple" ))+
theme_bw()
Exercise 3
ppi = 300
png("Exercise_3_graph.png", width=9*ppi, height=3*ppi, res = ppi)
# SNPs %>%
ggplot(SNPs,aes(chromosome, fill = genotype))+
geom_bar(position = "fill")+
theme_bw()Exercise 3
Exercise 4
png("Exercise_4_graph.png", width=6*ppi, height=3*ppi, res = ppi)
SNPs %>%
ggplot(.,aes(chromosome, fill = genotype))+
geom_bar(position = "fill")+
theme_bw()+
labs(x ="Chromosomes ")+
facet_wrap(vars(genotype))Exercise 4
Exersise 5
ggplotly(
SNPs %>%
ggplot(.,aes(chromosome, fill = genotype))+
geom_bar(position = "fill")+
theme_bw()+
labs(x ="Chromosomes ")+
facet_wrap(vars(genotype))
)datatable(SNPs)## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html